Predicting Growth Potential for Under-Developed Countries in the World

Author

George Charalambous

Load the Materials

library(tidyverse)
library(here)
library(maps)
library(plotly)
library(colorspace)
world_df <- map_data("world")
electricity_access <- read_csv("data/access_to_electricity/access_to_electricity.csv", 
                        skip = 4) |>
  select(-c(3:34), -c(67:69))
countries <- c("Afghanistan", "Angola", "Bangladesh", "Benin", "Burkina Faso", "Burundi", "Cambodia", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Djibouti", "Eritrea", "Ethiopia", "Gambia, The", "Guinea", "Guinea-Bissau", "Haiti", "Kiribati", "Lao PDR", "Lesotho", "Liberia", "Madagascar", "Malawi", "Mali", "Mauritania", "Mozambique", "Myanmar", "Nepal", "Niger", "Rwanda", "Sao Tome and Principe", "Senegal", "Sierra Leone", "Solomon Islands", "Somalia", "South Sudan", "Sudan", "Tanzania", "Timor-Leste", "Togo", "Tuvalu", "Uganda", "Yemen, Rep.", "Zambia")

Experimenting for the Model

underdeveloped_electricity_stats <- electricity_access |>
  filter(`Country Name` %in% countries) |>
  pivot_longer(c(3:34), 
               names_to = "Year", 
               values_to = "Electricity Access") |>
  filter(!is.na(`Electricity Access`)) |>
  group_by(`Country Name`) |>
  summarise(`mean` = mean(`Electricity Access`),
            `sd` = sd(`Electricity Access`))

Experimenting for the Shiny App

underdeveloped_electricity <- electricity_access |>
  filter(`Country Name` %in% countries) |>
  pivot_longer(c(3:34), 
               names_to = "Year", 
               values_to = "Electricity Access") |>
  filter(!is.na(`Electricity Access`)) 
underdeveloped_map <- left_join(underdeveloped_electricity, world_df, by = c("Country Name"="region"))
plot_1 <- ggplot()+
  geom_polygon(data = world_df, mapping = aes(x = long, y = lat, group = group), fill = "grey")+
  geom_polygon(data = underdeveloped_map, mapping = aes(x = long, y = lat, group = group, fill = `Electricity Access`, label = `Country Name`))+
  scale_fill_continuous_sequential(palette = "Heat")+
  theme_minimal()

ggplotly(plot_1, tooltip = "label")
agricultural_land <- read_csv("data/agricultural_land/agricultural_land.csv", 
                       skip = 4) |>
  select(-c(3:34), -c(67:69))
freshwater_withdrawals <- read_csv("data/annual_freshwater_withdrawals/freshwater_withdrawals.csv", 
                            skip = 4) |>
  select(-c(3:34), -c(67:69))
atms <- read_csv("data/atms/atms.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
precipitation <- read_csv("data/avg_precipitation/precipitation_depth.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
sanitation <- read_csv("data/basic_sanitation_services/basic_sanitation.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
broad_money <- read_csv("data/broad_money/broad_money.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
agr_employment <- read_csv("data/employment_in_agr/employment_in_agr.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
fertility_rate <- read_csv("data/fertility_rate/fertility_rate.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
gov_debt <- read_csv("data/gov_debt/central_gov_debt.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
internet <- read_csv("data/internet/internet.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
birth_life_exp <- read_csv("data/life_expectancy_birth/life_expectancy_birth.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
poverty <- read_csv("data/poverty_headcount/poverty_headcount_ratio.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))
school_enrol <- read_csv("data/school_enrollment/school_enrollment.csv", 
          skip = 4) |>
  select(-c(3:34), -c(67:69))

Variable Description

Variable Description
Access to Electricity The percentage of population with access to electricity
Agricultural land The share of land area that is arable, under permanent crops, and under permanent pastures
Annual freshwater withdrawals Total water withdrawals, not counting evaporation losses from storage basins
Automated teller machines (ATMs) Automated teller machines per 100,000 adults
Average precipitation in depth the long-term average in depth (over space and time) of annual precipitation in mm per year
Basic Sanitation Services percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households
Broad money The sum of currency outside banks, as a fraction of the GDP
Employment in agriculture The percentage of total employment engaged in agriculture
Fertility rate The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year
Central government debt The entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date
Individual Use of Internet The percentage of population that uses the internet
Life expectancy at birth The number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life
Poverty headcount ratio The percentage of the population living below the national poverty line(s)
School enrollment, primary and secondary (gross) The ratio of girls to boys enrolled at primary and secondary levels in public and private schools